{"title":"A study on instance-based learning with reduced training prototypes for device-context-independent activity recognition on a mobile phone","authors":"S. Thiemjarus, Apiwat Henpraserttae, S. Marukatat","doi":"10.1109/BSN.2013.6575462","DOIUrl":null,"url":null,"abstract":"This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device orientations. The transformation matrix is estimated on a ten-second dynamic data buffer. To search for a suitable set of training prototypes for iPhone implementation, an activity recognition experiment is conducted with twenty different device contexts performed by eight subjects. With the developed mobile application, the recognition results along with the user's location can be displayed on both iPhone and the web application in real time.","PeriodicalId":138242,"journal":{"name":"2013 IEEE International Conference on Body Sensor Networks","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Body Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BSN.2013.6575462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 30
Abstract
This paper presents a study of two simple methods for reducing the complexity of the instance-based classification technique and demonstrates their use in device-context independent activity recognition on a mobile phone. A projection-based method for signal rectification has been implemented on an iPhone in order to handle with variation in device orientations. The transformation matrix is estimated on a ten-second dynamic data buffer. To search for a suitable set of training prototypes for iPhone implementation, an activity recognition experiment is conducted with twenty different device contexts performed by eight subjects. With the developed mobile application, the recognition results along with the user's location can be displayed on both iPhone and the web application in real time.
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